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sequences, analyse those data using Bayesian, Maximum Likelihood and coalescence approaches, and build matrices of geolocation and morphological data. The work will be alongside others working on related
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systems , Quantum Transport in topological phases , Radiation Therapy Simulation , RF Engineering , Scattering amplitudes , simulation-based inference , single- and multicomponent quantum hydrodynamics
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expression and developability. Propose and validate optimization tools for performing (Bayesian) design of experiments. System validation and iterative refinement based on empirical data. Test and refine
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university. More information about us, please visit: the Department of Biochemistry and Biophysics . Project description Project title: Biology-informed Robust AI Methods for Inferring Complex Gene Regulatory
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ethnomycology or ethnobiology large-scale (ethnographic) database construction phylogenetic comparative analyses with Bayesian computational tools The applicant must have the ability to work independently and in
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history. It helps in the inference of the reward values by taking the observed history of policies as input. As the priorities are represented as utilities based on the multiple rewards [4].[5]; techniques
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and/or Python. Experience in, and aptitude for, complex statistical modelling (inc. mixed effects regression models and/or Bayesian statistics). Excellent written and spoken English. Desirable (traits
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main project by addressing specific case studies or specific targeted techniques. The main tools to be used will come from the discipline of Machine Learning, particularly those based on Bayesian methods
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prevalent is polygraph testing which infers deception through the measurement and analysis of physiological responses (e.g., blood pressure, electrodermal activity). However, despite its widespread
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). The goals are to develop new computational methods that allow the scientific inference of explainable attributes to describe human behavior and self-reports as well as to make progress in the computational